Find research datasets worth reusing

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2000): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2000. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2003): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2003. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2021): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2021. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2020): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2020. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2013): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2013. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2016): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2016. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2005): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2005. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2011): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2011. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2012): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2012. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2010): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2010. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2009): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2009. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2006): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2006. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2002): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2002. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2014): Reflectances bands, NDVI and NDWI

<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2014. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-09-01/2022-10-31): Spectral indices

<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-09-01/2022-10-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-07-01/2022-08-31): Spectral indices

<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-07-01/2022-08-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-03-01/2022-04-30): Spectral indices

<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-03-01/2022-04-30. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2022-01-01/2022-02-28): Spectral indices

<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2022-01-01/2022-02-28. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-11-01/2000-12-31): Spectral indices

<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2000-11-01/2000-12-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>

Landsat-based Spectral Indices for pan-EU 2000-2022 - Bimonthly predictor (2000-09-01/2000-10-31): Spectral indices

<h2><strong>Data information</strong></h2> <p>This dataset includes widely used indices for 2000-09-01/2000-10-31. It covers key aspects such as vegetation, crops, soil, and water, available bimonthly (i.e. one value per two months).</p> <ul> <li><strong>Normalized Difference Vegetation Index (NDVI)</strong> is used to evaluate vegetation health and biomass. It is calculated as <code>(nir - red) / (nir + red)</code> (<a href="https://www.sciencedirect.com/science/article/pii/0034425779900130?via%3Dihub">Tucker, 1979</a>).</li> <li><strong>Normalized Difference Tillage Index (NDTI)</strong>, also known as Normalized Burn Ratio 2 (NBR2), used in tillage detection, post-fire recovery studies, and soil sealing identification. It is calculated as <code>(swir1 - swir2) / (swir1 + swir2)</code> (<a href="https://www.asprs.org/wp-content/uploads/pers/1997journal/jan/1997_jan_87-93.pdf">Van Deventer et al., 1997</a>).</li> <li><strong>Soil Adjusted Vegetation Index (SAVI)</strong> is more effective in areas with sparse vegetation by minimizing the impact of soil brightness on vegetation sensing. It is calculated as <code>((nir - red) / (nir + red + 0.5)) * 1.5</code> (<a href="https://www.sciencedirect.com/science/article/pii/003442578890106X">Huete, 1988</a>).</li> <li><strong>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</strong> provides a more direct measurement of plant productivity. It is calculated as <code>((ndvi - 0.03) * (0.95 - 0.001)) / (0.96 - 0.03) + 0.001</code> (<a href="https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.74">Robinson et al., 2018</a>).</li> <li><strong>Normalized Difference Water Index (NDWI)</strong> provides insights into water dynamics and climatic characteristics. It is calculated as <code>(nir - swir1) / (nir + swir1)</code> (<a href="https://www.sciencedirect.com/science/article/pii/S0034425796000673">Gao, 1996</a>).</li> <li><strong>Normalized Difference Snow Index (NDSI)</strong> helps identify snowy areas. It is calculated as <code>(green - swir1) / (green + swir1)</code> (<a href="https://ieeexplore.ieee.org/abstract/document/1645275/">Salomonson and Appel, 2006</a>).</li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>